Palavras-chave
Qualidade De Serviços
Ensino Superior
Adoção De Tecnologia
Variáveis Moderadoras
Como Citar
Resumo
Objetivo: Este estudo tem como objetivo analisar como as percepções dos estudantes sobre a qualidade dos serviços universitários influenciam a assimilação de ferramentas de inteligência artificial (IA) no ensino superior, considerando os efeitos moderadores de idade, gênero, nível de estudo e país de estudo.
Metodologia: Adotou-se uma abordagem quantitativa, com coleta de dados por meio de survey aplicada a estudantes do ensino superior. Os construtos foram operacionalizados com base em escalas consolidadas na literatura, incluindo dimensões da qualidade percebida dos serviços e assimilação de IA. Os dados foram analisados por meio de testes de confiabilidade e validade, análise de correlação e análise de moderação para verificação das hipóteses propostas.
Resultados: Os resultados indicam que a qualidade percebida dos serviços universitários exerce efeito positivo e significativo sobre a assimilação da inteligência artificial pelos estudantes. Além disso, variáveis moderadoras como idade, gênero, nível de estudo e país influenciam parcialmente essa relação, evidenciando a relevância de fatores demográficos e contextuais na adoção de tecnologias emergentes no ensino superior.
Originalidade/Contribuições: O estudo contribui para a literatura ao integrar os conceitos de qualidade de serviços e assimilação de inteligência artificial em um modelo teórico unificado, ampliando as discussões sobre adoção tecnológica no contexto educacional. Ademais, fornece evidências empíricas sobre o papel de variáveis moderadoras, oferecendo subsídios para a formulação de estratégias institucionais voltadas à promoção de ambientes educacionais orientados por IA.
Referências
Ali MY, Naeem SB, Bhatti R, et al. (2024). Artificial Intelligence Applications in University Libraries of Pakistan: A SWOT Analysis and Implications. Global Knowledge, Memory and Communication 73: 219–234. http://dx.doi.org/10.1108/GKMC-12-2021-0203.
Alkurdi, A. A., Asaad, R. R., Almufti, S. M., & Ahmed, N. S. (2024). Evaluating the impact of point-biserial correlation-based feature selection on machine learning classifiers: a credit card fraud detection case study. Revista Gestão & Tecnologia, 24(Special), 166-196.
Al-Samarraie, H., Sarsam, S., Alzahrani, A., Chatterjee, A., & Swinnerton, B. (2024). Gender Perceptions of Generative AI in Higher Education. Journal of Applied Research in Higher Education. https://doi.org/10.1108/jarhe-02-2024-0109.
Ambika G Dr. V. Priya, (2025). Perception on Usage of Artificial Intelligence Tools among B.Ed. Students. International Journal of Emerging Knowledge Studies. https://doi.org/10.70333/ijeks-04-02-s-015.
Amruta, Ms., & Raman, Dr. R. (2024). Transforming International Student Admissions with Artificial Intelligence: Strategies and Outcomes. 1, 1–5. https://doi.org/10.1109/ickecs61492.2024.10617335.
Arowosegbe, A., Alqahtani, J., & Oyelade, T. (2024). Perception of Generative AI Use in UK Higher Education. Frontiers in Education. https://doi.org/10.3389/feduc.2024.1463208.
Balasa, K., Dumagay, A., Alieto, E., & Vallejo, R. (2025). Gender and Age Dynamics in Future Educators' Attitudes toward AI Integration in Education: A Sample from State-managed Universities in Zamboanga Peninsula, Philippines. Seminars in Medical Writing and Education. https://doi.org/10.56294/mw2025668.
Bell, E., Bryman, A., & Harley, B. (2022). Sampling in Qualitative Research. Oxford University Press. https://doi.org/10.1093/hebz/9780198869443.003.0030.
Black, R. W., & Tomlinson, B. (2025). University students describe how they adopt AI for writing and research in a general education course. Scientific reports, 15(1), 8799. https://doi.org/10.1038/s41598-025-92937-2.
Bouranta, N., Psomas, E. L., & Kafetzopoulos, D. (2025). Integrating online learning into service quality assessment in higher education: its influence on student satisfaction. The TQM Journal, 37(5), 1418-1445. https://doi.org/10.1108/TQM-06-2023-0180.
Breuninger, J. (2023). Introduction to Statistics for HCI Using Jamovi: A course for HCI practitioners and researchers on inferential statistics and Jamovi, an open-source statistics software, comparable to IBM SPSS. https://doi.org/10.1145/3544549.3574164.
Cachero, C., Tomás, D., & Pujol, F. (2025). Gender Bias in Self-Perception of Artificial Intelligence Knowledge, Impact, and Support Among Higher Education Students: An Observational Study. ACM Transactions on Computing Education. https://doi.org/10.1145/3721295.
Calle-Díaz, D. M., Porras-Cruz, F. L., & Santamaría Freire, E. J. (2024). Modelo de aceptación tecnológica y la difusión de contenidos en estudiantes universitarios. MQRInvestigar, 8(4), 5685–5705. https://doi.org/10.56048/mqr20225.8.4.2024.5685-5705.
Cappelli, P., & Conyon, M. J. (2018). What do performance appraisals do?. ILR Review, 71(1), 88–116. https://doi.org/10.1177/0019793917698649.
Chan, C., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20. https://doi.org/10.1186/s41239-023-00411-8.
Chaudhary, A. A., Arif, S., Calimlim, R. F., Khan, S. Z., & Sadia, A. (2024). The impact of ai-powered educational tools on student engagement and learning outcomes at higher education level. International Journal of Contemporary Issues in Social Sciences, 3(2), 2842-2852.
Cho, C., & Ofosu-Anim, D. (2025). Exploring international students' perceptions of adopting generative artificial intelligence (GenAI) technologies in learning. Social Sciences & Humanities Open, 11, 101418. https://doi.org/10.1016/j.ssaho.2025.101418.
Çınkır, Ş., Yıldız, S., & Kurum, G. (2022). The Effect of Undergraduate Students’ Perceived Service Quality on Student Commitment. SAGE Open, 12. https://doi.org/10.1177/21582440221096134.
Cordero, J., Torres-Zambrano, J., & Cordero-Castillo, A. (2024). Integration of Generative Artificial Intelligence in Higher Education: Best Practices. Education Sciences, 15(1), 32. https://doi.org/10.3390/educsci15010032.
Fošner, A. (2024). University students’ attitudes and perceptions towards AI tools: implications for sustainable educational practices. Sustainability, 16(19), 8668.
Gulzar, S., Din, F. U., Noor, S., & Anwar, M. M. (2024). Exploring how cultural backgrounds influence teaching methods, student expectations, and educational success across different societies. Bull. Bus. Econ, 13(3), 211-218. http://dx.doi.org/10.61506/01.00479.
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable operations and computers, 3, 275–285. https://doi.org/10.1016/j.susoc.2022.05.004.
Hamid, I. A., & Mohd Noor, N. H. (2024). Customer satisfaction with cafeteria service quality: Assessment of SERVQUAL on the satisfaction of public sector employees. BERJAYA Journal of Services & Management, 22, 1-13.
Haverila, M., Haverila, K., McLaughlin, C., & Arora, M. (2021). Towards a comprehensive student satisfaction model. The International Journal of Management Education. https://doi.org/10.1016/j.ijme.2021.100558.
Hyesun Choung, Prabu David, Arun Ross (2022). Trust in AI and Its Role in the Acceptance of AI Technologies. Volume(39), 1727-1739. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2022.2050543.
Ifekanandu, C. C., Anene, J. N., Iloka, C. B., & Ewuzie, C. O. (2023). The Influence of Artificial Intelligence (AI) on Customer Experience and Loyalty: A Mediating Role of Personalization. Journal of Data Acquisition and Processing, 38(3), 1936. https://doi.org/10.5281/zenodo.98549423.
Isabell Runge, Florian Hebibi, Rebecca Lazarides (2025). Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model. Education Sciences. https://www.semanticscholar.org/paper/d8caa4bd4c239ba5d3af937bf31ff7c042cfcf8d.
Kamak, R., Debo, D., & Saaida, M. (2024, June). Challenges of AI in higher education.
Kotsis, K. T. (2025). Issues between artificial intelligence and personal data in education. Int Res Educ, 13(1), 45-65. https://doi.org/10.5296/ire.v13i1.22850.
Lampou, R., Noor, M., Vivekanantharasa, R., & Patnaik, S. (2026). AI–Driven Educational Ecosystems: Integrating Learning Analytics, Adaptive Assessment, and Intelligent Feedback for Sustainable Student Performance. Qubahan Techno Journal, 5(1), 25-36. https://doi.org/10.48161/qtj.v5n1a80.
Lan, M., & Zhou, X. (2025). A Qualitative Systematic Review of AI-Enabled Self-Regulated Learning in Higher Education. NPJ Science of Learning, 10. https://doi.org/10.1038/s41539-025-00319-0.
Lim, G. F. C., Jalil, N. A., Hidup, D. S. A., Omar, M., Kamaruzaman, F. M., & Majid, M. Z. A. (2023). The Use of Google Forms in Teaching and Learning based on Teachers’ Perspective. International Journal of Academic Research in Business & Social Sciences. https://doi.org/10.6007/ijarbss/v13-i12/20221.
Marcello M. Mariani, Isa Machado, Vittoria Magrelli, Yogesh K. Dwivedi (2022). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Volume(122), 102623-102623. Technovation. https://doi.org/10.1016/j.technovation.2022.102623.
Matobobo, C. (2026). A systematic review of gender differences in students’ use of AI tools for learning in higher education. Discover Education. https://doi.org/10.1007/s44217-026-01116-6?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle.
Melnyk Y. B., Pypenko I. S. (2025). Implementing of Artificial Intelligence in a Higher Educational Ecosystem. https://doi.org/10.26697/ijsa.2025.1.1.
Michael Gerlich (2023). Perceptions and Acceptance of Artificial Intelligence: A Multi-Dimensional Study. Volume(12), 502-502. Social Sciences. https://doi.org/10.3390/socsci12090502.
Mohammed Ahmed Abou Adel, Moustafa Mohamed Abouelnour, Mohammad Issa Alhoura, Asmaa Awad (2025). Towards Intelligent Universities Enhanced with Artificial Intelligence (AI). https://doi.org/10.24294/jipd10412
Ofosu-Ampong, K. (2023). Gender Differences in Perception of Artificial Intelligence-Based Tools. Journal of Digital Art & Humanities. https://doi.org/10.33847/2712-8149.4.2_6.
Owusu, S., Zimpa, J., Atta, F., & Darling, M. (2024). Evaluating the Impact of AI-Personalized Learning Systems in Higher Education; Examining how They Affect Academic Performance across Different Age Groups at Kumasi Technical University. Aug-Sept 2024. https://doi.org/10.55529/jaimlnn.45.19.29.
Paposa, K. K., & Paposa, S. S. (2023). From brick to click classrooms: A paradigm shift during the pandemic—Identifying factors influencing service quality and learners’ satisfaction in click classrooms. Management and Labour Studies, 48(2), 182-196. https://doi.org/10.1177/0258042X211066234.
Richter, F. (2025). The ethical acceptability of personalization via intelligent systems in education. AI & SOCIETY, 1-15. https://doi.org/10.1007/s00146-025-02690-3.
Russo, C., Romano, L., Clemente, D., Iacovone, L., Gladwin, T. E., & Panno, A. (2025). Gender Differences in Artificial Intelligence: The Role of Artificial Intelligence Anxiety. Frontiers in Psychology, 16, 1559457. https://doi.org/10.3389/fpsyg.2025.1559457.
Russo, C., Romano, L., Clemente, D., Iacovone, L., Gladwin, T. E., & Panno, A. (2025). Gender differences in artificial intelligence: the role of artificial intelligence anxiety. Frontiers in Psychology, 16, 1559457. https://doi.org/10.3389/fpsyg.2025.1559457.
Ryzheva, N., Nefodov, D., Romanyuk, S., Marynchenko, H., & Kudla, M. (2024). Artificial Intelligence in higher education: opportunities and challenges. Revista Amazonia Investiga, 13(73), 284-296. https://doi.org/10.34069/AI/2024.73.01.24.
Sage Kelly, Sherrie-Anne Kaye, Óscar Oviedo-Trespalacios (2022). What factors contribute to the acceptance of artificial intelligence? A systematic review. Volume(77), 101925-101925. Telematics and Informatics. https://doi.org/10.1016/j.tele.2022.101925.
Saleem, S., Aziz, M. U., Iqbal, M. J., & Abbas, S. (2025). AI in education: Personalized learning systems and their impact on student performance and engagement. The critical review of social sciences studies, 3(1), 2445-2459. https://doi.org/10.59075/c35qa453.
Sardesai, S., D’Souza, E., & Govekar, S. (2024). Analysing the impacts of artificial intelligence service quality and human service quality on customer satisfaction and customer loyalty in the hospitality sector. Turizam, 28(1), 37-48. https://doi.org/10.5937/turizam28-45450.
Sean Sands, Colin Campbell, Kirk Plangger, Carla Ferraro (2022). Unreal influence: leveraging AI in influencer marketing. Volume(56), 1721-1747. European Journal of Marketing. https://doi.org/10.1108/ejm-12-2019-0949.
Sebopelo, P., Baloyi, O., & Chukwuma, N. N. (2025). Artificial Intelligence assimilation and University Service Quality: The Mediating Role of student satisfaction. Review of Artificial Intelligence in Education, 6, e042. https://doi.org/10.37497/rev.artif.intell.educ.v6ii.42
Sebopelo , P., Baloyi , O., & Chukwuma , N. N. (2025). Artificial Intelligence assimilation and University Service Quality: The Mediating Role of student satisfaction . SDGs Studies Review , 6(studies), e033. https://doi.org/10.37497/sdgs.v6istudies.33
Sentia, T., Mustafia, M., & Zuraidah, E. (2022). Analisa Kualitas Layanan Pada E-learning di Sekolah Menggunakan Metode Servqual. Journal of Informatics Management and Information Technology, 2(3), 100–108. https://doi.org/10.47065/jimat.v2i3.167.
Singh, H. P. (2025). Artificial Intelligence in Education: Transforming Learning for the Future. Available at SSRN 5375205. https://doi.org/10.2139/ssrn.5375205.
Sova, R., Tudor, C., Tartavulea, C. V., & Dieaconescu, R. I. (2024). Artificial intelligence tool adoption in higher education: A structural equation modeling approach to understanding impact factors among economics students. Electronics, 13(18), 3632. https://doi.org/10.3390/electronics13183632.
Soylu, M. Y., Lee, J., Hung, J. T., Cui, C. Z., & Joyner, D. A. (2025). AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind. arXiv preprint arXiv:2507.21654. https://doi.org/10.48550/arXiv.2507.21654.
Stöhr, C., Ou, A. W., & Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, 100259. https://doi.org/10.1016/j.caeai.2024.100259.
Strzelecki, A., & ElArabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. Br. J. Educ. Technol., 55, 1209-1230. https://doi.org/10.1111/bjet.13425.
Sultan, P., & Wong, H. (2018). How service quality affects university brand performance, university brand image and behavioural intention: the mediating effects of satisfaction and trust and moderating roles of gender and study mode. Journal of Brand Management, 26, 332-347. https://doi.org/10.1057/S41262-018-0131-3.
Testa, M., Della Volpe, M., D’Amato, A., & Apuzzo, A. (2024). Does gender impact the relationship between perceived value and intentions of use of natural processing models?. Transforming Government: People, Process and Policy.
Thorpe, G. E. (2022). Relationship Between Perceived Quality of Student Learning Experience, Perceived Quality of Mentoring, and Student Satisfaction (Doctoral dissertation, Walden University).
Tominc, P., & Rožman, M. (2023). Artificial intelligence and business studies: differences in study cycles regarding perceptions of key future competences. Education Sciences, 13(6), 580. https://doi.org/10.3390/educsci13060580.
Toscano-Hernández, A. E., Álvarez-González, L. I., Sanzo-Pérez, M. J., & Esparza Rodríguez, S. A. (2024). Service quality in higher education: A systematic literature review, 2007-2023. Estudios Gerenciales, 40(170), 13-30. https://doi.org/10.18046/j.estger.2024.170.6244.
Toscano-Hernández, A. E., Álvarez-González, L. I., Sanzo-Pérez, M. J., & Esparza Rodríguez, S. A. (2024). Service quality in higher education: A systematic literature review, 2007-2023. Estudios Gerenciales, 40(170), 13-30.
Tzirides, A. O., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A. R., You, Y., dos Santos, T. A., Searsmith, D., O’Brien, C., Cope, B., & Kalantzis, M. (2024). Combining Human and Artificial Intelligence for Enhanced AI Literacy in Higher Education. Computers and Education Open. https://doi.org/10.1016/j.caeo.2024.100184.
Vázquez-Parra, J. C., Henao-Rodríguez, C., Lis-Gutiérrez, J. P., & Palomino-Gámez, S. (2024). Importance of university students’ perception of adoption and training in artificial intelligence tools. Societies, 14(8), 141. https://doi.org/10.3390/soc14080141?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle.
Yasemin Darancik, Erdal Kaçar, Aişe Sezik (2025). AI anxiety and awareness of German teacher candidates. Education and Information Technologies. https://www.semanticscholar.org/paper/132db070494c1304d7080a583a300ec8719b3b85.
Yogesh K. Dwivedi, Laurie Hughes, Abdullah M. Baabdullah, Samuel Ribeiro‐Navarrete, Mihalis Giannakis, Mutaz M. Al‐Debei, Denis Dennehy, et al. (2022). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. Volume(66), 102542-102542. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2022.102542.
Younes, S., Alharbi, A., & Aboeldahab, M. (2021). Measuring the Impact of an AI-Enabled Mobile Application for University Students. Mob. Inf. Syst., 2021, 4141576:1-4141576:12. https://doi.org/10.1155/2021/4141576.
Yu, J. H., & Chauhan, D. (2025). Trends in NLP for personalized learning: LDA and sentiment analysis insights. Education and Information Technologies, 30(4), 4307-4348. https://doi.org/10.1007/s10639-024-12988-2?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle.
Yuntao Wang, Zhou Su, Ning Zhang, Rui Xing, Dongxiao Liu, Tom H. Luan, Xuemin Shen (2022). A Survey on Metaverse: Fundamentals, Security, and Privacy. Volume(25), 319-352. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2022.3202047.
Zapata‐Rivera, D., Torre, I., Lee, C., Sarasa Cabezuelo, A., Ghergulescu, I., & Libbrecht, P. (2024). Editorial: Generative AI in education. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1532896.
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... & Li, Y., 2021. A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021, 1-18. https://doi.org/10.1155/2021/8812542.
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M., (2023). Acceptance of Artificial Intelligence among Preservice Teachers: A Multigroup Analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00420-7.
Zhang, K., & Aslan, A. B., (2021). AI technologies for education: Recent research & future directions. Computers and Education Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025.
Zou, Y., Kuek, F., Feng, W., & Cheng, X. (2025, March). Digital learning in the 21st century: trends, challenges, and innovations in technology integration. In Frontiers in Education (Vol. 10, p. 1562391). Frontiers Media SA. https://doi.org/10.3389/feduc.2025.1562391.
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Phineas Sebopelo, Nnenna Nancy Chukwuma